SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents

Deep reinforcement learning algorithms (DRL) are increasingly being used in safety-critical systems. Ensuring the safety of DRL agents is a critical concern in such contexts. However, relying solely on testing is not sufficient to ensure safety as it does not offer guarantees. Building safety monitors is one solution to alleviate this challenge. This paper proposes SMARLA, a machine learning-based safety monitoring approach designed for DRL agents. For practical reasons, SMARLA is designed to be black-box (as it does not require …

agents algorithms building cs.ai cs.lg cs.se monitoring monitors reinforcement reinforcement learning safety safety-critical solution systems testing